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This article looks at the historical construction of knowledge about the elderly body from a medical perspective that is concerned with the materiality of the body and associated losses. It recalls, presents and analyses the paradigm of loss, decline and failure that dominates the way care is provided, and examines the issues associated with this domination. By presenting old age as a social construct produced by language and subject to values relating to a certain performance of the body, the author invites us to shift our perspective and take a finer, more complex and broader view of the body and the experience of being old.

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OBJECTIVE: Concerns exist about clinical and operative skill decay in surgery residents when they dedicate time away from clinical training to pursue research. However, it remains undetermined how to best prevent these negative impacts. Our study evaluated the perspectives of surgical research residents on interventions to improve their reentry into clinical training. DESIGN, SETTING, AND PARTICIPANTS: An anonymous web-based survey was distributed between 5/01/2023 and 6/01/2023 to 102 current and former (within the previous 3 years) general surgery research residents from 4 academic medical centers in Boston, MA. RESULTS: Survey response rate was 35.3% (36/102 residents). About 22 of 36 residents (61.1%) felt that their clinical aptitude decreased during the research years, whereas 33 of 36 (91.7%) reported reduced surgical skills. When reflecting on their re-entry to residency, former research residents reported feeling anxious and less confident (3.84/5 on a 1-5 Likert scale) as well as being below the expected level of clinical performance (3.42/5). Most of them (12 of 17; 70.6%) reported that it took up to 6 months, whereas 5 of them (29.4%) up to 12 months to feel at the expected level. When compared to nonmoonlighting residents, those who moonlighted often and operated during moonlighting, denied a decrease in clinical and surgical skills, and reported less anxiety, higher confidence, and a quicker return to the expected level of performance. Interventions proposed for improving their clinical re-entry included individualized development plans for 3 months before returning to clinical training, established curriculum for clinical work throughout the research years, clinical preceptorships throughout the research years, and simulation curriculum throughout the research years. CONCLUSIONS: General surgery residents feel that their clinical and surgical skills decreased during the research years, leading to anxiety and lack of confidence when returning to residency. Therefore, comprehensive interventions are needed to improve the reentry of the research residents into clinical training.

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THE CHALLENGE: A basic science curriculum is foundational to surgical training. However, engagement in, and satisfaction with, standard lectures is notoriously poor and antithetical to adult education principles. THE TOOL: We developed a worksheet-based delivery system to improve participation and engagement in basic science. METHODS: The SCORE curriculum objectives were used to create interactive worksheets that could be filled out in advance and were reviewed as a group during protected education time. All PGY levels in our community-based academically affiliated program participated (19 residents total). The worksheets were created by a resident in her research year and vetted by the attending moderating each session. The same amount of time allotted for our former basic science lectures was used for worksheet review as a group. The frequency of active resident participation was recorded before and after implementing the worksheet-based curriculum. A survey was created to determine resident satisfaction with the curriculum and was sent out before and after the implementation of the new curriculum. Mean resident survey scores with lecture-based and worksheet-based curriculum were compared utilizing paired t-tests. RESULTS: Fourteen residents responded to the survey. With the lecture-based format, active participation was noted 30% of the time or less. With the worksheet-based format, engagement increased to 100% of the time. Additionally, there was a statistically significant increase in the resident opinion of the utility of the sessions, congruence with their learning style, and improvement in knowledge. About 64% of the residents who completed the survey either "always" or "sometimes" completed the worksheets ahead of time and 92% of the residents who completed the worksheets ahead of time felt like it allowed them to increase their engagement in didactics. CONCLUSIONS: Engagement is a core tenet of adult education and changing from lectures to worksheets in the delivery of basic science capitalizes on this concept, leading to increased resident satisfaction. This initiative is easily translatable to any type of residency program and can be completed within the currently allotted timeframe for the basic science conference.

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OBJECTIVE: Aim: This article is aimed at raising awareness and stimulating scientific discussion on the necessity of involving qualified medical professionals in conducting criminal procedural actions that involve intervention in human somatic rights, in order to further improve the legal instruments ensuring compliance with the European Court of Human Rights (hereinafter referred to as the ECHR) standards in this field. PATIENTS AND METHODS: Materials and Methods: In preparing the article, the following issues were worked out: the provisions of international legal acts; legal positions of the ECHR related to the use of medical knowledge in the criminal process; scientific studies of various aspects of the use of medical knowledge in the criminal process. The methodological basis of the research is dialectical, comparative-legal, systemic-structural, analytical, synthetic, complex research methods. CONCLUSION: Conclusions: The use of medical knowledge in the criminal process generally takes two forms: (a) expert and (b) ancillary. The expert form, particularly forensic medical examination, must adhere to a set of criteria reflected in the practice of the ECHR. Personal searches involving penetration into human body cavities generally align with the requirements of the he European Convention on Human Rights (hereinafter referred to as the Convention), provided certain conditions are met, including medical considerations. The criterion for the admissibility of coercive collection of biological samples for examination is the existence of samples independent of the individual's will.

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BACKGROUND: This study aims to facilitate parental identification of designated emergency facilities for expeditious pediatric care within the framework of Taiwan's newly implemented "regional joint defense" approach to pediatric emergency services. The research seeks to elucidate the mechanisms by which this novel system can enhance timely access to appropriate emergency care for children, potentially improving health outcomes and resource utilization in acute pediatric situations. METHODS: Factor analysis (FA) and triangular entropy matrix (TEM) analyzed the appearance, breathing and skin of pediatric assessment triangle (ABC of PAT), three types of prehospital pediatric emergence condition (PPEC), five levels of Taiwan's pediatric emergency triage (TPET), and applied the social learning theory (SLT) in educational doctrine, using experts' weighted questionnaires. RESULTS: Firstly, to address deficiencies in Taiwan's pediatric prehospital emergency medicine (PEM) system, integrating emergency medical knowledge (EMK) and pediatric life support (PLS) into medical education, staff training, and the national handbook for new parents is crucial. This equips parents to manage children's illnesses and prevent emergencies. Then, in life-threatening situations, immediate emergency room (ER) transport is vital for symptoms like whitish or purple lips, cold limbs, mottled skin, cold sweat, convulsions, dyspnea, chest dimples, weak consciousness, and oxygen saturation below 94%. Finally, for non-life-threatening emergencies, seek medical evaluation if symptoms include wheezing, chest tightness, chest pain, persistent high fever over 39 degrees with convulsions, chills, cold sweats, not eating or urinating for over 12 hours, or fever lasting more than 48 hours. CONCLUSION: Parents must remain calm and provide their baby with a sense of security while observing the development of physical symptoms. This approach enables them to effectively determine the most appropriate time to take their children to the emergency room, thereby avoiding life-threatening emergencies. Prompt and proper measures and treatments not only alleviate various discomforts caused by illness or medical emergencies but also reduce systemic distress, life-threatening situations, and unfortunate incidents before hospitalization.

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Accurately assigning standardized diagnosis and procedure codes from clinical text is crucial for healthcare applications. However, this remains challenging due to the complexity of medical language. This paper proposes a novel model that incorporates extreme multi-label classification tasks to enhance International Classification of Diseases (ICD) coding. The model utilizes deformable convolutional neural networks to fuse representations from hidden layer outputs of pre-trained language models and external medical knowledge embeddings fused using a multimodal approach to provide rich semantic encodings for each code. A probabilistic label tree is constructed based on the hierarchical structure existing in ICD labels to incorporate ontological relationships between ICD codes and enable structured output prediction. Experiments on medical code prediction on the MIMIC-III database demonstrate competitive performance, highlighting the benefits of this technique for robust clinical code assignment.

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BACKGROUND: Despite their growing use in health care, pretrained language models (PLMs) often lack clinical relevance due to insufficient domain expertise and poor interpretability. A key strategy to overcome these challenges is integrating external knowledge into PLMs, enhancing their adaptability and clinical usefulness. Current biomedical knowledge graphs like UMLS (Unified Medical Language System), SNOMED CT (Systematized Medical Nomenclature for Medicine-Clinical Terminology), and HPO (Human Phenotype Ontology), while comprehensive, fail to effectively connect general biomedical knowledge with physician insights. There is an equally important need for a model that integrates diverse knowledge in a way that is both unified and compartmentalized. This approach not only addresses the heterogeneous nature of domain knowledge but also recognizes the unique data and knowledge repositories of individual health care institutions, necessitating careful and respectful management of proprietary information. OBJECTIVE: This study aimed to enhance the clinical relevance and interpretability of PLMs by integrating external knowledge in a manner that respects the diversity and proprietary nature of health care data. We hypothesize that domain knowledge, when captured and distributed as stand-alone modules, can be effectively reintegrated into PLMs to significantly improve their adaptability and utility in clinical settings. METHODS: We demonstrate that through adapters, small and lightweight neural networks that enable the integration of extra information without full model fine-tuning, we can inject diverse sources of external domain knowledge into language models and improve the overall performance with an increased level of interpretability. As a practical application of this methodology, we introduce a novel task, structured as a case study, that endeavors to capture physician knowledge in assigning cardiovascular diagnoses from clinical narratives, where we extract diagnosis-comment pairs from electronic health records (EHRs) and cast the problem as text classification. RESULTS: The study demonstrates that integrating domain knowledge into PLMs significantly improves their performance. While improvements with ClinicalBERT are more modest, likely due to its pretraining on clinical texts, BERT (bidirectional encoder representations from transformer) equipped with knowledge adapters surprisingly matches or exceeds ClinicalBERT in several metrics. This underscores the effectiveness of knowledge adapters and highlights their potential in settings with strict data privacy constraints. This approach also increases the level of interpretability of these models in a clinical context, which enhances our ability to precisely identify and apply the most relevant domain knowledge for specific tasks, thereby optimizing the model's performance and tailoring it to meet specific clinical needs. CONCLUSIONS: This research provides a basis for creating health knowledge graphs infused with physician knowledge, marking a significant step forward for PLMs in health care. Notably, the model balances integrating knowledge both comprehensively and selectively, addressing the heterogeneous nature of medical knowledge and the privacy needs of health care institutions.

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BACKGROUND: Interpretability and intuitive visualization facilitate medical knowledge generation through big data. In addition, robustness to high-dimensional and missing data is a requirement for statistical approaches in the medical domain. A method tailored to the needs of physicians must meet all the abovementioned criteria. OBJECTIVE: This study aims to develop an accessible tool for visual data exploration without the need for programming knowledge, adjusting complex parameterizations, or handling missing data. We sought to use statistical analysis using the setting of disease and control cohorts familiar to clinical researchers. We aimed to guide the user by identifying and highlighting data patterns associated with disease and reveal relations between attributes within the data set. METHODS: We introduce the attribute association graph, a novel graph structure designed for visual data exploration using robust statistical metrics. The nodes capture frequencies of participant attributes in disease and control cohorts as well as deviations between groups. The edges represent conditional relations between attributes. The graph is visualized using the Neo4j (Neo4j, Inc) data platform and can be interactively explored without the need for technical knowledge. Nodes with high deviations between cohorts and edges of noticeable conditional relationship are highlighted to guide the user during the exploration. The graph is accompanied by a dashboard visualizing variable distributions. For evaluation, we applied the graph and dashboard to the Hamburg City Health Study data set, a large cohort study conducted in the city of Hamburg, Germany. All data structures can be accessed freely by researchers, physicians, and patients. In addition, we developed a user test conducted with physicians incorporating the System Usability Scale, individual questions, and user tasks. RESULTS: We evaluated the attribute association graph and dashboard through an exemplary data analysis of participants with a general cardiovascular disease in the Hamburg City Health Study data set. All results extracted from the graph structure and dashboard are in accordance with findings from the literature, except for unusually low cholesterol levels in participants with cardiovascular disease, which could be induced by medication. In addition, 95% CIs of Pearson correlation coefficients were calculated for all associations identified during the data analysis, confirming the results. In addition, a user test with 10 physicians assessing the usability of the proposed methods was conducted. A System Usability Scale score of 70.5% and average successful task completion of 81.4% were reported. CONCLUSIONS: The proposed attribute association graph and dashboard enable intuitive visual data exploration. They are robust to high-dimensional as well as missing data and require no parameterization. The usability for clinicians was confirmed via a user test, and the validity of the statistical results was confirmed by associations known from literature and standard statistical inference.

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OBJECTIVE: Urological education has been declining in medical schools, leaving many students without adequate exposure to the fundamentals of the field. We aimed to create a virtual urology course for medical students preparing for subinternships. DESIGN: We created a 4-week curriculum of case-based urology modules with sections on hematuria, bladder cancer, kidney stones, vesicoureteral reflux, prostate cancer, urinary incontinence, and erectile dysfunction. Students completed precourse and postcourse surveys assessing confidence in content knowledge and 4 educational competencies. Faculty completed postcourse surveys. Confidence was scored on a 5-point Likert scale (0-4). SETTING: We offered the course in May 2022 and May 2023. The course was fully virtual and was offered at medical schools across the United States. PARTICIPANTS: The course included 157 medical students from 60 institutions and 44 faculty instructors from 30 institutions. All instructors were urologists representing a range of urological subspecialties. RESULTS: Surveys were completed by 61/157 students (39%) and 33/44 faculty (75%). Median student confidence in content knowledge increased across all disease processes: hematuria (3 vs. 2), bladder cancer (3 vs. 1), kidney stones (3 vs. 2), vesicoureteral reflux (3 vs. 1), prostate cancer (3 vs. 1), urinary incontinence (3 vs. 2), and erectile dysfunction (3 vs. 2) (all p < 0.001). Median confidence scores also increased across all 4 educational competencies: patient evaluation (3 vs. 2), pathophysiology (3 vs. 2), literature appraisal (3 vs. 2), and patient counseling (3 vs. 1) (all p < 0.001). Confidence increases in all areas were maintained at 7-month follow-up. Most students (85%) and faculty (91%) rated the course "excellent" or "very good." CONCLUSIONS: A multi-institutional virtual urology course for medical students led to a durable increase in confidence pertaining to content knowledge and various educational competencies.

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BACKGROUND: Clinical core medical knowledge (CCMK) learning is essential for medical trainees. Adaptive assessment systems can facilitate self-learning, but extracting experts' CCMK is challenging, especially using modern data-driven artificial intelligence (AI) approaches (e.g., deep learning). OBJECTIVES: This study aims to develop a multi-expert knowledge-aggregated adaptive assessment scheme (MEKAS) using knowledge-based AI approaches to facilitate the learning of CCMK in otolaryngology (CCMK-OTO) and validate its effectiveness through a one-month training program for CCMK-OTO education at a tertiary referral hospital. METHODS: The MEKAS utilized the repertory grid technique and case-based reasoning to aggregate experts' knowledge to construct a representative CCMK base, thereby enabling adaptive assessment for CCMK-OTO training. The effects of longitudinal training were compared between the experimental group (EG) and the control group (CG). Both groups received a normal training program (routine meeting, outpatient/operation room teaching, and classroom teaching), while EG received MEKAS for self-learning. The EG comprised 22 UPGY trainees (6 postgraduate [PGY] and 16 undergraduate [UGY] trainees) and 8 otolaryngology residents (ENT-R); the CG comprised 24 UPGY trainees (8 PGY and 16 UGY trainees). The training effectiveness was compared through pre- and post-test CCMK-OTO scores, and user experiences were evaluated using a technology acceptance model-based questionnaire. RESULTS: Both UPGY (z = -3.976, P < 0.001) and ENT-R (z = -2.038, P = 0.042) groups in EG exhibited significant improvements in their CCMK-OTO scores, while UPGY in CG did not (z = -1.204, P = 0.228). The UPGY group in EG also demonstrated a substantial improvement compared to the UPGY group in CG (z = -4.943, P < 0.001). The EG participants were highly satisfied with the MEKAS system concerning self-learning assistance, adaptive testing, perceived satisfaction, intention to use, perceived usefulness, perceived ease of use, and perceived enjoyment, rating it between an overall average of 3.8 and 4.1 out of 5.0 on all scales. CONCLUSIONS: The MEKAS system facilitates CCMK-OTO learning and provides an efficient knowledge aggregation scheme that can be applied to other medical subjects to efficiently build adaptive assessment systems for CCMK learning. Larger-scale validation across diverse institutions and settings is warranted further to assess MEKAS's scalability, generalizability, and long-term impact.

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BACKGROUND: Sharing knowledge among scientists during global health emergencies is a critical issue. So, this study investigates knowledge-sharing behavior and attitude among staff members of 19 Medical schools in Egyptian universities during the COVID-19 pandemic. METHODS: Across-sectional study was conducted using a web-based questionnaire. A total of 386 replies from the 10,318 distributed questionnaires were analyzed. Descriptive statistics were computed using SPSS (version 22) to summarize the demographic data. Inferential statistics such as the independent and chi-square test were used to achieve the study aims. RESULTS: More than half of the respondents (54.4%) indicated that their levels of knowledge of COVID-19 were good. Most participants (72.5%) reported that scientific publications and international websites were the most reliable source of their knowledge concerning COVID-19. More than 46% stated they sometimes share their knowledge. The lack of time to share and organizational culture were the most important factors that could affect their knowledge sharing. Additionally, about 75% of participants shared knowledge about treatment.

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Diagnostic errors are prevalent in critical care practice and are associated with patient harm and costs for providers and the healthcare system. Patient complexity, illness severity, and the urgency in initiating proper treatment all contribute to decision-making errors. Clinician-related factors such as fatigue, cognitive overload, and inexperience further interfere with effective decision-making. Cognitive science has provided insight into the clinical decision-making process that can be used to reduce error. This evidence-based review discusses ten common misconceptions regarding critical care decision-making. By understanding how practitioners make clinical decisions and examining how errors occur, strategies may be developed and implemented to decrease errors in Decision-making and improve patient outcomes.

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BACKGROUND: Quality and accuracy of online scientific data are crucial, given that the internet and social media serve nowadays as primary sources of medical knowledge. OBJECTIVE: This study aims to analyze the relationship between scientific relevance and online visibility of strabismus research to answer the following questions: (1) Are the most popular strabismus papers scientifically relevant? (2) Are the most high-impact strabismus studies shared enough online? METHODS: The Altmetric Attention Score (AAS) was used as a proxy for online visibility, whereas citations and the journal's impact factor (IF) served as a metric for scientific relevance. Using "strabismus" as a keyword, 100 papers with the highest AAS and 100 papers with the highest number of citations were identified. Statistical analyses, including the Spearman rank test, linear regression, and factor analysis, were performed to assess the relationship between AAS, citations, a journal's IF, and mentions across 18 individual Web 2.0 platforms. RESULTS: A weak, positive, statistically significant correlation was observed between normalized AAS and normalized citations (P<.001; r=0.27) for papers with high visibility. Only Twitter mentions and Mendeley readers correlated significantly with normalized citations (P=.02 and P<.001, respectively) and IF (P=.04 and P=.009, respectively), with Twitter being the strongest significant predictor of citation numbers (r=0.53). For high-impact papers, no correlation was found between normalized citations and normalized AAS (P=.12) or the IF of the journal (P=.55). CONCLUSIONS: While clinical relevance influences online attention, most high-impact research related to strabismus is not sufficiently shared on the web. Therefore, researchers should make a greater effort to share high-impact papers related to strabismus on online media platforms to improve accessibility and quality of evidence-based knowledge for patients.

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Background: The launch of the Chat Generative Pre-trained Transformer (ChatGPT) in November 2022 has attracted public attention and academic interest to large language models (LLMs), facilitating the emergence of many other innovative LLMs. These LLMs have been applied in various fields, including healthcare. Numerous studies have since been conducted regarding how to employ state-of-the-art LLMs in health-related scenarios to assist patients, doctors, and public health administrators. Objective: This review aims to summarize the applications and concerns of applying conversational LLMs in healthcare and provide an agenda for future research on LLMs in healthcare. Methods: We utilized PubMed, ACM, and IEEE digital libraries as primary sources for this review. We followed the guidance of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRIMSA) to screen and select peer-reviewed research articles that (1) were related to both healthcare applications and conversational LLMs and (2) were published before September 1st, 2023, the date when we started paper collection and screening. We investigated these papers and classified them according to their applications and concerns. Results: Our search initially identified 820 papers according to targeted keywords, out of which 65 papers met our criteria and were included in the review. The most popular conversational LLM was ChatGPT from OpenAI (60), followed by Bard from Google (1), Large Language Model Meta AI (LLaMA) from Meta (1), and other LLMs (5). These papers were classified into four categories in terms of their applications: 1) summarization, 2) medical knowledge inquiry, 3) prediction, and 4) administration, and four categories of concerns: 1) reliability, 2) bias, 3) privacy, and 4) public acceptability. There are 49 (75%) research papers using LLMs for summarization and/or medical knowledge inquiry, and 58 (89%) research papers expressing concerns about reliability and/or bias. We found that conversational LLMs exhibit promising results in summarization and providing medical knowledge to patients with a relatively high accuracy. However, conversational LLMs like ChatGPT are not able to provide reliable answers to complex health-related tasks that require specialized domain expertise. Additionally, no experiments in our reviewed papers have been conducted to thoughtfully examine how conversational LLMs lead to bias or privacy issues in healthcare research. Conclusions: Future studies should focus on improving the reliability of LLM applications in complex health-related tasks, as well as investigating the mechanisms of how LLM applications brought bias and privacy issues. Considering the vast accessibility of LLMs, legal, social, and technical efforts are all needed to address concerns about LLMs to promote, improve, and regularize the application of LLMs in healthcare.

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OBJECTIVES: Large language models, including ChatGPT, has the potential to transform the way we approach medical knowledge, yet accuracy in clinical topics is critical. Here we assessed ChatGPT's performance in adhering to the American Academy of Otolaryngology-Head and Neck Surgery guidelines. METHODS: We presented ChatGPT with 24 clinical otolaryngology questions based on the guidelines of the American Academy of Otolaryngology. This was done three times (N = 72) to test the model's consistency. Two otolaryngologists evaluated the responses for accuracy and relevance to the guidelines. Cohen's Kappa was used to measure evaluator agreement, and Cronbach's alpha assessed the consistency of ChatGPT's responses. RESULTS: The study revealed mixed results; 59.7% (43/72) of ChatGPT's responses were highly accurate, while only 2.8% (2/72) directly contradicted the guidelines. The model showed 100% accuracy in Head and Neck, but lower accuracy in Rhinology and Otology/Neurotology (66%), Laryngology (50%), and Pediatrics (8%). The model's responses were consistent in 17/24 (70.8%), with a Cronbach's alpha value of 0.87, indicating a reasonable consistency across tests. CONCLUSIONS: Using a guideline-based set of structured questions, ChatGPT demonstrates consistency but variable accuracy in otolaryngology. Its lower performance in some areas, especially Pediatrics, suggests that further rigorous evaluation is needed before considering real-world clinical use.

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OBJECTIVE: Didactic education in General Surgery (GS) residency typically follows a nationally standardized curriculum; however, instructional format varies by institution. In recent years, GS residents at our institution expressed discontentment with weekly didactics and were not meeting their goals on the American Board of Surgery In-Training Examination (ABSITE). We sought to develop improvements in our didactic curriculum to increase resident satisfaction and ABSITE scores of GS junior residents (Jrs). DESIGN: In a quality improvement project, we changed the weekly didactic curriculum format from hour-long lectures in the 2018 to 2019 academic year (AY) to a partially-flipped classroom in the 2019 to 2020 AY, involving a 30-minute faculty-led presentation followed by 30 minutes of resident-led practice questions. The outcomes measured were ABSITE scores taken in 2019 and 2020 and resident opinions via an anonymous survey. SETTING: This study was conducted at the University of Minnesota (Minneapolis, MN). PARTICIPANTS: The cohort for this study included all GS Jrs in our GS residency program, including postgraduate year (PGY) 1 nondesignated preliminary, PGY1 to 3 categorical GS residents, and residents in their lab time. Senior residents attended a separate didactics session. RESULTS: After curriculum changes, the ABSITE percentile scores for GS Jrs rose from 52% ± 5% to 66% ± 4% (p = 0.03). No categorical GS Jr scored <30% in 2020, compared to 20% (6/30) of categorical General Surgery residents in 2019. All residents preferred the new format overall and reported greater engagement in and preparation for didactics. CONCLUSIONS: After changing didactic education from hour-long lectures in the 2018 to 2019 AY to a flipped classroom model in the 2019 to 2020 AY including 30 minutes of faculty-led lecture followed by 30 minutes of resident-led practice questions, ABSITE scores and resident satisfaction at the University of Minnesota General Surgery Program improved.

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OBJECTIVE: Entrustable professional activities (EPAs) are a crucial component of contemporary postgraduate medical education with many surgery residency programs having implemented EPAs as a competency assessment framework to assess and provide feedback on the performance of their residents. Despite broad implementation of EPAs, there is a paucity of evidence regarding the impact of EPAs on the learners and learning environments. A first step in improving understanding of the use and impact of EPAs is by mapping the rising number of EPA-related publications from the field of surgery. The primary objective of this scoping review is to examine the nature, extent, and range of articles on the development, implementation, and assessment of EPAs. The second objective is to identify the experiences and factors that influence EPA implementation and use in practice in surgical specialties. DESIGN: Scoping review. Four electronic databases (Medline, Embase, Education Source, and ERIC) were searched on January 20, 2022, and then again on July 19, 2023. A quasi-statistical content analysis was employed to quantify and draw meaning from the information related to the development, implementation, assessment, validity, reliability, and experiences with EPAs in the workplace. PARTICIPANTS: A total of 42 empirical and nonempirical articles were included. RESULTS: Four thematic categories describe the topic areas in included articles related to: 1) the development and refinement of EPAs, including the multiple steps taken to develop and refine unique EPAs for surgery residency programs; 2) the methods for implementing EPAs; 3) outcomes of EPA use in practice; 4) barriers, facilitators, and areas for improvement for the implementation and use of EPAs in surgical education. CONCLUSIONS: This scoping review highlights the key trends and gaps from the rapidly increasing number of publications on EPAs in surgery residency, from development to their use in the workplace. Existing EPA studies lack a theoretical and/or conceptual basis; future development and implementation studies should adopt implementation science frameworks to better structure and operationalize EPAs within surgery residency programs.

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The construction of medical knowledge graphs (MKGs) is steadily progressing from manual to automatic methods, which inevitably introduce noise, which could impair the performance of downstream healthcare applications. Existing error detection approaches depend on the topological structure and external labels of entities in MKGs to improve their quality. Nevertheless, due to the cost of manual annotation and imperfect automatic algorithms, precise entity labels in MKGs cannot be readily obtained. To address these issues, we propose an approach named Enhancing error detection on Medical knowledge graphs via intrinsic labEL (EMKGEL). Considering the absence of hyper-view KG, we establish a hyper-view KG and a triplet-level KG for implicit label information and neighborhood information, respectively. Inspired by the success of graph attention networks (GATs), we introduce the hyper-view GAT to incorporate label messages and neighborhood information into representation learning. We leverage a confidence score that combines local and global trustworthiness to estimate the triplets. To validate the effectiveness of our approach, we conducted experiments on three publicly available MKGs, namely PharmKG-8k, DiseaseKG, and DiaKG. Compared with the baseline models, the Precision@K value improved by 0.7%, 6.1%, and 3.6%, respectively, on these datasets. Furthermore, our method empirically showed that it significantly outperformed the baseline on a general knowledge graph, Nell-995.

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Diagnosis prediction, a key factor in enhancing healthcare efficiency, remains a focal point in clinical decision support research. However, the time-series, sparse and multi-noise characteristics of electronic health record (EHR) data make it a great challenge. Existing methods commonly address these issues using RNNs and incorporating medical prior knowledge from medical knowledge bases, but they neglect the local spatial characteristics and spatial-temporal correlation of the data. Consequently, we propose MDPG, a diagnosis prediction model based on patient knowledge graphs. Initially, we represent the electronic visit records of patients as a patient-centered temporal knowledge graph, capturing the local spatial structure and temporal characteristics of the visit information. Subsequently, we design the spatial graph convolution block, temporal self-attention block, and spatial-temporal synchronous graph convolution block to capture the spatial, temporal, and spatial-temporal correlations embedded in them, respectively. Ultimately, we accomplish the prediction of patients' future states through multi-label classification. We conduct comprehensive experiments on two real-world datasets independently and evaluate the results using visit-level precision@k and code-level accuracy@k metrics. The experimental results demonstrate that MDPG outperforms all baseline models, yielding the best performance.